






import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


SCREAMING_SNAKE_CASE__		:							List[Any] 				=				logging.getLogger(__name__)

SCREAMING_SNAKE_CASE__		:							List[str] 				=				"""pytorch_model.bin"""







@dataclasses.dataclass
class 				UpperCAmelCase_  :
     __lowerCamelCase      				=  dataclasses.field(
         metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'}		)
     __lowerCamelCase      				=  dataclasses.field(
         default=__lowerCamelCase							,      metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'}							,      )







@dataclasses.dataclass
class 				UpperCAmelCase_  :
     __lowerCamelCase      				=  dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'}		)
     __lowerCamelCase      				=  dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'}		)
     __lowerCamelCase      				=  dataclasses.field(
         default=__lowerCamelCase							,      metadata={'help': 'A csv or a json file containing the validation data.'}		)
     __lowerCamelCase      				=  dataclasses.field(
         default=__lowerCamelCase							,      metadata={'help': 'The name of the task to train on.'}							,      )
     __lowerCamelCase      				=  dataclasses.field(
         default=__lowerCamelCase							,      metadata={'help': 'The list of labels for the task.'}		)







@dataclasses.dataclass
class 				UpperCAmelCase_  :
     __lowerCamelCase      				=  dataclasses.field(
         metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'}		)
     __lowerCamelCase      				=  dataclasses.field(
         default='accuracy'							,      metadata={'help': 'The evaluation metric used for the task.'}		)
     __lowerCamelCase      				=  dataclasses.field(
         default='no'							,      metadata={
             'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
         }							,      )
     __lowerCamelCase      				=  dataclasses.field(
         default=10							,      metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}							,      )
     __lowerCamelCase      				=  dataclasses.field(
         default=0.0							,      metadata={
             'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
         }							,      )
     __lowerCamelCase      				=  dataclasses.field(
         default=__lowerCamelCase							,      metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}							,      )
     __lowerCamelCase      				=  dataclasses.field(
         default=__lowerCamelCase							,      metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}							,      )
     __lowerCamelCase      				=  dataclasses.field(
         default=__lowerCamelCase							,      metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'}							,      )
     __lowerCamelCase      				=  dataclasses.field(
         default=0.0							,      metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'}							,      )
     __lowerCamelCase      				=  dataclasses.field(
         default=100							,      metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}							,      )
     __lowerCamelCase      				=  dataclasses.field(
         default=__lowerCamelCase							,      metadata={'help': 'Random seed for initialization.'}							,      )





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase       ) ->			List[str]:

     '''simple docstring'''
     UpperCAmelCase__      :  int				      =			datasets.concatenate_datasets([infer_input, infer_output] ,							axis=1       )

     if args.do_filter_by_confidence:
          UpperCAmelCase__      :  Any				      =			dataset.filter(lambda __lowerCamelCase       : example["probability"] > args.confidence_threshold       )

     if args.do_filter_by_val_performance:
          assert eval_result >= 0.0 and eval_result <= 1.0
          UpperCAmelCase__      :  int				      =			int(eval_result * len(__lowerCamelCase       )       )
          print(__lowerCamelCase       )
          UpperCAmelCase__      :  Optional[int]				      =			dataset.sort("""probability""" ,							reverse=__lowerCamelCase       )
          UpperCAmelCase__      :  Dict				      =			dataset.select(range(__lowerCamelCase       )       )

     UpperCAmelCase__      :  str				      =			dataset.remove_columns(["""label""", """probability"""]       )
     UpperCAmelCase__      :  Any				      =			dataset.rename_column("""prediction""" ,							"""label"""       )
     UpperCAmelCase__      :  int				      =			dataset.map(lambda __lowerCamelCase       : {"label": idalabel[example["label"]]}       )
     UpperCAmelCase__      :  Tuple				      =			dataset.shuffle(seed=args.seed       )

     UpperCAmelCase__      :  Dict				      =			os.path.join(__lowerCamelCase ,							F"train_pseudo.{args.data_file_extension}"       )
     if args.data_file_extension == "csv":
          dataset.to_csv(__lowerCamelCase ,							index=__lowerCamelCase       )
     else:
          dataset.to_json(__lowerCamelCase       )







def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							**__lowerCamelCase       ) ->			Optional[int]:

     '''simple docstring'''
     UpperCAmelCase__      :  Dict				      =			Accelerator()
     # Make one log on every process with the configuration for debugging.
     logging.basicConfig(
         format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,							datefmt="""%m/%d/%Y %H:%M:%S""" ,							level=logging.INFO ,							)
     logger.info(accelerator.state       )

     # Setup logging, we only want one process per machine to log things on the
     # screen. accelerator.is_local_main_process is only True for one process per
     # machine.
     logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR       )

     if accelerator.is_local_main_process:
          datasets.utils.logging.set_verbosity_warning()
          transformers.utils.logging.set_verbosity_info()
     else:
          datasets.utils.logging.set_verbosity_error()
          transformers.utils.logging.set_verbosity_error()

     UpperCAmelCase__      :  str				      =			STModelArguments(model_name_or_path=__lowerCamelCase       )
     UpperCAmelCase__      :  int				      =			STDataArguments(train_file=__lowerCamelCase ,							infer_file=__lowerCamelCase       )
     UpperCAmelCase__      :  Dict				      =			STTrainingArguments(output_dir=__lowerCamelCase       )
     UpperCAmelCase__      :  Dict				      =			argparse.Namespace()

     for arg_class in (model_args, data_args, training_args):
          for key, value in vars(__lowerCamelCase       ).items():
               setattr(__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase       )

     for key, value in kwargs.items():
          if hasattr(__lowerCamelCase ,							__lowerCamelCase       ):
               setattr(__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase       )

    # Sanity checks
     UpperCAmelCase__      :  Tuple				      =			{}
     UpperCAmelCase__      :  Dict				      =			None

     # You need to provide the training data and the data to predict on
     assert args.train_file is not None
     assert args.infer_file is not None
     UpperCAmelCase__      :  Tuple				      =			args.train_file
     UpperCAmelCase__      :  List[str]				      =			args.infer_file

     if args.evaluation_strategy != IntervalStrategy.NO.value:
          assert args.eval_file is not None
          UpperCAmelCase__      :  Union[str, Any]				      =			args.eval_file

     for key in data_files:
          UpperCAmelCase__      :  List[str]				      =			data_files[key].split("""."""       )[-1]
          assert extension in ["csv", "json"], F"`{key}_file` should be a csv or a json file."
          if args.data_file_extension is None:
               UpperCAmelCase__      :  Any				      =			extension
          else:
               assert extension == args.data_file_extension, F"`{key}_file` should be a {args.data_file_extension} file`."

     assert (
         args.eval_metric in datasets.list_metrics()
     ), F"{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."

     # If passed along, set the training seed now.
     if args.seed is not None:
          set_seed(args.seed       )

     logger.info("""Creating the initial data directory for self-training..."""       )
     UpperCAmelCase__      :  List[Any]				      =			F"{args.output_dir}/self-train_iter-{{}}".format
     UpperCAmelCase__      :  Any				      =			data_dir_format(0       )

     if accelerator.is_main_process:
          if args.output_dir is not None:
               os.makedirs(args.output_dir ,							exist_ok=__lowerCamelCase       )
               os.makedirs(__lowerCamelCase ,							exist_ok=__lowerCamelCase       )
     accelerator.wait_for_everyone()

     UpperCAmelCase__      :  Union[str, Any]				      =			None
     UpperCAmelCase__      :  Tuple				      =			None
     UpperCAmelCase__      :  Dict				      =			0
     UpperCAmelCase__      :  Optional[int]				      =			False
     # Show the progress bar
     UpperCAmelCase__      :  int				      =			tqdm(range(args.max_selftrain_iterations       ) ,							disable=not accelerator.is_local_main_process       )

     # Self-train
     for iteration in range(0 ,							int(args.max_selftrain_iterations       )       ):
          UpperCAmelCase__      :  Tuple				      =			data_dir_format(__lowerCamelCase       )
          assert os.path.exists(__lowerCamelCase       )

          # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
          # iteration > 0
          UpperCAmelCase__      :  List[str]				      =			os.path.join(__lowerCamelCase ,							"""stage-1"""       )
          UpperCAmelCase__      :  List[Any]				      =			{
              """accelerator""": accelerator,
              """model_name_or_path""": args.model_name_or_path,
              """cache_dir""": args.cache_dir,
              """do_train""": True,
              """train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
              """do_eval""": True if args.eval_file is not None else False,
              """eval_file""": data_files["""eval"""],
              """do_predict""": True,
              """infer_file""": data_files["""infer"""],
              """task_name""": args.task_name,
              """label_list""": args.label_list,
              """output_dir""": current_output_dir,
              """eval_metric""": args.eval_metric,
              """evaluation_strategy""": args.evaluation_strategy,
              """early_stopping_patience""": args.early_stopping_patience,
              """early_stopping_threshold""": args.early_stopping_threshold,
              """seed""": args.seed,
          }
          # Add additional training arguments
          for key, value in kwargs.items():
               if key not in arguments_dict and not hasattr(__lowerCamelCase ,							__lowerCamelCase       ):
                    arguments_dict.update({key: value}       )

          UpperCAmelCase__      :  List[Any]				      =			os.path.join(__lowerCamelCase ,							"""best-checkpoint""" ,							__lowerCamelCase       )
          if os.path.exists(__lowerCamelCase       ):
               logger.info(
                   """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.""" ,							__lowerCamelCase ,							__lowerCamelCase ,							)
          else:
               logger.info("""***** Running self-training: iteration: %d, stage: 1 *****""" ,							__lowerCamelCase       )
               finetune(**__lowerCamelCase       )
               accelerator.wait_for_everyone()
               assert os.path.exists(__lowerCamelCase       )
               logger.info("""Self-training job completed: iteration: %d, stage: 1.""" ,							__lowerCamelCase       )

          if iteration > 0 and args.finetune_on_labeled_data:
               # Stage 2 (optional): fine-tuning on the original labeled data
               UpperCAmelCase__      :  Any				      =			os.path.join(__lowerCamelCase ,							"""best-checkpoint"""       )
               UpperCAmelCase__      :  Any				      =			os.path.join(__lowerCamelCase ,							"""stage-2"""       )
               # Update arguments_dict
               UpperCAmelCase__      :  Union[str, Any]				      =			model_path
               UpperCAmelCase__      :  Optional[Any]				      =			data_files["""train"""]
               UpperCAmelCase__      :  Tuple				      =			current_output_dir

               UpperCAmelCase__      :  Union[str, Any]				      =			os.path.join(__lowerCamelCase ,							"""best-checkpoint""" ,							__lowerCamelCase       )
               if os.path.exists(__lowerCamelCase       ):
                    logger.info(
                        """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.""" ,							__lowerCamelCase ,							__lowerCamelCase ,							)
               else:
                    logger.info("""***** Running self-training: iteration: %d, stage: 2 *****""" ,							__lowerCamelCase       )
                    finetune(**__lowerCamelCase       )
                    accelerator.wait_for_everyone()
                    assert os.path.exists(__lowerCamelCase       )
                    logger.info("""Self-training job completed: iteration: %d, stage: 2.""" ,							__lowerCamelCase       )

          UpperCAmelCase__      :  str				      =			iteration
          UpperCAmelCase__      :  List[str]				      =			data_dir_format(iteration + 1       )

          UpperCAmelCase__      :  Any				      =			AutoConfig.from_pretrained(os.path.join(__lowerCamelCase ,							"""best-checkpoint"""       )       )
          UpperCAmelCase__      :  Optional[Any]				      =			config.idalabel
          UpperCAmelCase__      :  Tuple				      =			os.path.join(__lowerCamelCase ,							"""eval_results_best-checkpoint.json"""       )
          UpperCAmelCase__      :  Dict				      =			os.path.join(__lowerCamelCase ,							"""test_results_best-checkpoint.json"""       )
          assert os.path.exists(__lowerCamelCase       )

          with open(__lowerCamelCase ,							"""r"""       ) as f:
               UpperCAmelCase__      :  Optional[Any]				      =			float(json.load(__lowerCamelCase       )[args.eval_metric]       )
          UpperCAmelCase__      :  Tuple				      =			os.path.join(__lowerCamelCase ,							"""infer_output_best-checkpoint.csv"""       )
          assert os.path.exists(__lowerCamelCase       )
          # Loading the dataset from local csv or json files.
          UpperCAmelCase__      :  Union[str, Any]				      =			load_dataset(args.data_file_extension ,							data_files={"""data""": data_files["""infer"""]}       )["""data"""]
          UpperCAmelCase__      :  List[Any]				      =			load_dataset("""csv""" ,							data_files={"""data""": infer_output_file}       )["""data"""]

          if accelerator.is_main_process:
               os.makedirs(__lowerCamelCase ,							exist_ok=__lowerCamelCase       )
               shutil.copy(__lowerCamelCase ,							os.path.join(__lowerCamelCase ,							F"eval_results_iter-{iteration}.json"       )       )
               if os.path.exists(__lowerCamelCase       ):
                    shutil.copy(__lowerCamelCase ,							os.path.join(__lowerCamelCase ,							F"test_results_iter-{iteration}.json"       )       )
               create_pseudo_labeled_data(__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase       )
          accelerator.wait_for_everyone()

          UpperCAmelCase__      :  Tuple				      =			os.path.join(__lowerCamelCase ,							F"train_pseudo.{args.data_file_extension}"       )

          if args.evaluation_strategy != IntervalStrategy.NO.value:
               UpperCAmelCase__      :  Optional[Any]				      =			eval_result

               if best_iteration is None:
                    UpperCAmelCase__      :  Tuple				      =			new_iteration
                    UpperCAmelCase__      :  Tuple				      =			new_eval_result
               else:
                    if new_eval_result - best_eval_result > args.early_stopping_threshold:
                         UpperCAmelCase__      :  List[str]				      =			new_iteration
                         UpperCAmelCase__      :  List[str]				      =			new_eval_result
                         UpperCAmelCase__      :  Optional[int]				      =			0
                    else:
                         if new_eval_result == best_eval_result:
                              UpperCAmelCase__      :  Tuple				      =			new_iteration
                              UpperCAmelCase__      :  Union[str, Any]				      =			new_eval_result
                         early_stopping_patience_counter += 1

                    if early_stopping_patience_counter >= args.early_stopping_patience:
                         UpperCAmelCase__      :  List[Any]				      =			True

          progress_bar.update(1       )

          if should_training_stop:
               break

     if best_iteration is not None:
          # Save the best iteration
          logger.info("""Best iteration: %d""" ,							__lowerCamelCase       )
          logger.info("""Best evaluation result: %s = %f""" ,							args.eval_metric ,							__lowerCamelCase       )
          accelerator.wait_for_everyone()
          if accelerator.is_main_process:
               shutil.copy(
                   os.path.join(__lowerCamelCase ,							F"eval_results_iter-{iteration}.json"       ) ,							os.path.join(__lowerCamelCase ,							"""eval_results_best-iteration.json"""       ) ,							)
     else:
          # Assume that the last iteration is the best
          logger.info("""Best iteration: %d""" ,							args.max_selftrain_iterations - 1       )
          logger.info("""Best evaluation result: %s = %f""" ,							args.eval_metric ,							__lowerCamelCase       )
          accelerator.wait_for_everyone()
          if accelerator.is_main_process:
               shutil.copy(
                   os.path.join(__lowerCamelCase ,							F"eval_results_iter-{args.max_selftrain_iterations - 1}.json"       ) ,							os.path.join(__lowerCamelCase ,							"""eval_results_best-iteration.json"""       ) ,							)







"""simple docstring"""



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Optional[int]									=			{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class  snake_case__      (       snake_case_       ):
 _snake_case       :					Union[str, Any]							  =						"""sew-d"""


 def __init__( self    ,		lowerCamelCase=32    ,		lowerCamelCase=768    ,		lowerCamelCase=12    ,		lowerCamelCase=12    ,		lowerCamelCase=3072    ,		lowerCamelCase=2    ,		lowerCamelCase=512    ,		lowerCamelCase=256    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=("p2c", "c2p")    ,		lowerCamelCase="layer_norm"    ,		lowerCamelCase="gelu_python"    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.0    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.02    ,		lowerCamelCase=1E-7    ,		lowerCamelCase=1E-5    ,		lowerCamelCase="group"    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,		lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,		lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,		lowerCamelCase=False    ,		lowerCamelCase=128    ,		lowerCamelCase=16    ,		lowerCamelCase=True    ,		lowerCamelCase=0.05    ,		lowerCamelCase=10    ,		lowerCamelCase=2    ,		lowerCamelCase=0.0    ,		lowerCamelCase=10    ,		lowerCamelCase=0    ,		lowerCamelCase="mean"    ,		lowerCamelCase=False    ,		lowerCamelCase=False    ,		lowerCamelCase=256    ,		lowerCamelCase=0    ,		lowerCamelCase=1    ,		lowerCamelCase=2    ,		**lowerCamelCase    ,		):
        super().__init__(**lowerCamelCase    ,		pad_token_id=lowerCamelCase    ,		bos_token_id=lowerCamelCase    ,		eos_token_id=lowerCamelCase						)
        __a       =    hidden_size
        __a       =    feat_extract_norm
        __a       =    feat_extract_activation
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    conv_bias
        __a       =    num_conv_pos_embeddings
        __a       =    num_conv_pos_embedding_groups
        __a       =    len(self.conv_dim						)
        __a       =    num_hidden_layers
        __a       =    intermediate_size
        __a       =    squeeze_factor
        __a       =    max_position_embeddings
        __a       =    position_buckets
        __a       =    share_att_key
        __a       =    relative_attention
        __a       =    norm_rel_ebd
        __a       =    list(lowerCamelCase						)
        __a       =    hidden_act
        __a       =    num_attention_heads
        __a       =    hidden_dropout
        __a       =    attention_dropout
        __a       =    activation_dropout
        __a       =    feat_proj_dropout
        __a       =    final_dropout
        __a       =    layer_norm_eps
        __a       =    feature_layer_norm_eps
        __a       =    initializer_range
        __a       =    vocab_size

        if (
            (len(self.conv_stride						) != self.num_feat_extract_layers)
            or (len(self.conv_kernel						) != self.num_feat_extract_layers)
            or (len(self.conv_dim						) != self.num_feat_extract_layers)
        ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect."
                   "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                   F"but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)"
                   F"= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`."						)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __a       =    apply_spec_augment
        __a       =    mask_time_prob
        __a       =    mask_time_length
        __a       =    mask_time_min_masks
        __a       =    mask_feature_prob
        __a       =    mask_feature_length
        __a       =    mask_feature_min_masks

        # ctc loss
        __a       =    ctc_loss_reduction
        __a       =    ctc_zero_infinity

        # sequence classification
        __a       =    use_weighted_layer_sum
        __a       =    classifier_proj_size






 @property
 def 						a__   ( self						):
        return functools.reduce(operator.mul    ,		self.conv_stride    ,		1						)
